3.4.2.3 Biological and technical replicates

​​​​​​​​​​​​​A. Background & Definitions

As biological experiments can be complicated, technical replicate measurements are often taken to monitor the performance of the experiment. However, such technical replicates are not independent tests of the hypothesis, and so they cannot provide evidence of the reproducibility of the main results. Pseudoreplication in its various forms is a common error in the design and analysis of biological research.

• The term “pseudoreplication” can be defined as "the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent" (1).
• Biological replicates: parallel measurements of biologically distinct samples that capture random biological variation, which may itself be a subject of study or a source of noise. Biological replicates address if and how widely the results of an experiment can be generalized. For example, repeating a particular assay with independently generated samples, or samples derived from various cell types, tissue types, or organisms, to see if similar results can be observed.
• Technical replicates: repeated measurements of the same sample that represent independent measures of the random noise associated with protocols or equipment. Technical replicates address the reproducibility of the assay or technique, but not the reproducibility of the effect or event being studied. ​

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B. Guidance & Expectations

• Typically, biological variability is substantially greater than technical variability, so committing resources to sampling biologically relevant variables is usually beneficial (unless measures of technical variability are themselves of interest)
• Good experimental design practice includes planning for replication:
  • First, identify the questions the experiment aims to answer
  • Next, determine the proportion of variability induced by each step to distribute the capacity for replication of the experiment across steps
  • Be aware of the potential for pseudoreplication and aim to design statistically independent replicates
• To differentiate between technical and biological replicates, a useful question to ask is whether or not the follow-up test should give, in expectation, exactly the same quantitative result as the original study. Technical replication tests do not introduce independency into the experimental system and can mainly be applied to measure errors in sample handling as the new findings should be quantitatively identical to the old results. In contrast, true biological replicates are based on independent raw materials (animals, cells, etc.) and therefore do not need to give the same exact results as obtained before.

​ Further recommendations that are supported by a simulation, which can be found HERE, are:

• There is no reason to use the antiquated method of repeated measures (RM) ANOVA; in contrast to RM ANOVA, mixed effects modeling makes no sphericity assumption and handles missing data well.
• There is no reason to use nested ANOVA in this context: nesting is applicable in situations when one or another constraint does not allow crossing every level of one factor with every level of another factor. In such situations with a nested layout, fewer than all levels of one factor occur within each level of the other factor.
• The expanded descriptive summary can be highly instructive (and is yours to use freely).
• And last but not least, whatever method is used for the analysis, one should be maximally transparent about how the data were collected, what were the experimental units, what were the replicates, and what analyses were used to examine the data.

​PLEASE DO NOT FORGET

• ​For biologically distinct conditions, averaging technical replicates can limit the impact of measure­ment error, but taking additional biological replicates is often preferable for improving the efficiency of statistical testing.
• Technical replicates are an important internal quality control indicating how the experiment was performed - but can not be used to infer conclusions
• Avoiding pseudoreplication (defined by Hurlbert as "the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent"): the independence of observations or samples is (in most cases) an essential requirement on which statistical methods rely. Analyzing pseudoreplicated samples ultimately results in erroneous confidence intervals, that are too small, and inaccurate p-values due to underestimating the underlying experimental variability and the degrees of freedom (number of independent observations).

C. Resources

• Vaux, DL et al., Replicates and repeats-what is the difference and is it significant? EMBO Rep. 2012 Apr; 13(4): 291–296. [1]
• Blainey P et al., Points of Significance: Replication. Nature Methods 2014, 11(9): 879-880. [2]
• Hurlbert SH, Ecol Monogr. 1984, 54: 187-211​ [3]

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